What Determines the Rank and Dimension of a Matrix's Solution Space?

In summary, a matrix is a rectangular array used for organizing and manipulating data. The rank of a matrix is the maximum number of linearly independent rows or columns, which can be determined by reducing the matrix to row-echelon form. The dimension of a matrix refers to the number of rows and columns it contains. To multiply matrices, the number of columns in the first matrix must equal the number of rows in the second matrix.
  • #1
dylanpuw
1
0
(a)Determine the row rank of the matrix,

1 1 1 1
1 1 2 5
2 2 0 -6

(b) What is the column rank of this matrix?
(c) What is the dimension of the solution space Mx=0

So this is my answer:

I have reduced my matrix into echelon form and i get

1 1 1 1
0 0 -1 -4
0 0 0 0

Therefore my row rank is 2 (the number of linearly independent rows)

Since by rank theorem, (row rank = column rank = determinental rank) the column rank is also 2.

And the dimension of the solution space is 2 (number of columns - rank)

Is this answer correct?

Thank you
Dylan
 
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  • #2
yes, i think so
 

1. What is a matrix?

A matrix is a mathematical object that is represented by a rectangular array of numbers or symbols. It is used to organize and manipulate data in a structured way.

2. What is the rank of a matrix?

The rank of a matrix is the maximum number of linearly independent rows or columns in the matrix. It is a measure of the matrix's dimensionality and is useful in determining the properties of the matrix.

3. How is the rank of a matrix determined?

The rank of a matrix can be determined by performing row operations on the matrix until it is in reduced row-echelon form. The number of non-zero rows in the reduced matrix is equal to the rank of the original matrix.

4. What is the dimension of a matrix?

The dimension of a matrix refers to the number of rows and columns it contains. For example, a matrix with 3 rows and 4 columns would have a dimension of 3x4.

5. How do you multiply matrices?

To multiply two matrices, the number of columns in the first matrix must be equal to the number of rows in the second matrix. The result is a new matrix where each element is the sum of the products of the corresponding elements from the two original matrices.

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